At present, due to the development of computer technology, and particularly of image processing technology, image processing technology comes in to use in more and more fields for facilitating work. In some traditional fields, such as steel manufacturing, image processing technology has also been widely used.
As a widely used material, steel plate is a very important industrial product in the steel industry, and during the production of the steel plate, quality control is undoubtedly a very important part. In the production environment of traditional iron and steel enterprises, an important method of controlling the quality is to test the surface state of a steel plate to determine whether there is a flaw or defect in the steel plate, and to process the steel plate according to the test result. In traditional steel enterprises, there are two main types of quality control systems for detecting a defect: the first one only depends on manual quality control, which relies on industry experts to visually observe the photos of the production environment to give a detecting result; and the second one is manual quality control with machine assistance, which mainly filters out the photos without a defect by the quality control system capable of performing the detection, and then an industry expert detects the photos seemingly with a defect. With the second quality control system, automation in a certain extent may be achieved, and it is possible to determine whether there is a defect in a steel plate according to certain features in an image of the steel plate. However, features and determining rules are based on experience and embedded into a machine, so it is difficult to be upgraded with the development of the business, and as a result, detection accuracy of the system gets lower and lower with the development of the production process, and can even be completely unavailable. The quality control system needs to be modified during an upgrade, which is costly. Moreover, with traditional quality control, it is only possible to detect whether there is a defect in the steel plate and the detection accuracy is low.